An improved Hurst parameter estimator based on fractional Fourier transform

A fractional Fourier transform (FrFT) based estimation method is introduced in this paper to analyze the long range dependence (LRD) in time series. The degree of LRD can be characterized by the Hurst parameter. The FrFT-based estimation of Hurst parameter proposed in this paper can be implemented e...

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Bibliographic Details
Published in:Telecommunication systems 2010-04, Vol.43 (3-4), p.197-206
Main Authors: Chen, YangQuan, Sun, Rongtao, Zhou, Anhong
Format: Article
Language:English
Subjects:
Artificial Intelligence
Business and Management
Communications networks
Computer Communication Networks
Fourier analysis
Fourier transforms
IT in Business
Methods
Noise
Parameter estimation
Probability Theory and Stochastic Processes
Signal processing
Studies
Telecommunications systems
Time series
Wavelet transforms
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Summary:A fractional Fourier transform (FrFT) based estimation method is introduced in this paper to analyze the long range dependence (LRD) in time series. The degree of LRD can be characterized by the Hurst parameter. The FrFT-based estimation of Hurst parameter proposed in this paper can be implemented efficiently allowing very large data set. We used fractional Gaussian noises (FGN) which typically possesses long-range dependence with known Hurst parameters to test the accuracy of the proposed Hurst parameter estimator. For justifying the advantage of the proposed estimator, some other existing Hurst parameter estimation methods, such as wavelet-based method and a global estimator based on dispersional analysis, are compared. The proposed estimator can process the very long experimental time series locally to achieve a reliable estimation of the Hurst parameter.
ISSN:1018-4864
1572-9451
DOI:10.1007/s11235-009-9207-4